Forensic Data Analyst

About the role

The Forensic Data Analyst is responsible for collecting, preserving and analysing complex transactional, operational and digital data to detect fraud, financial crime and regulatory non‑compliance. The role transforms digital evidence into clear, reliable intelligence for investigations, management decisions, litigation and regulatory engagement.

Key responsibilities

• Acquire data securely from enterprise systems, databases, cloud platforms and email environments, applying forensic extraction methods and maintaining chain of custody.
• Use SQL, Python, R and forensic tools to identify anomalies, hidden patterns, duplicate or irregular transactions and behavioural fraud indicators.
• Design, document and maintain the annual fraud‑detection and continuous monitoring programme, developing and governing rules, thresholds and use cases.
• Translate complex findings into clear reports, dashboards, visualisations and evidence packages for management, legal counsel, regulators and law enforcement.
• Partner with investigators, business owners, IT, Legal and Risk & Compliance to define data requirements, recommend control improvements and ensure secure evidence handling.

Required profile

• Bachelor’s degree in a quantitative field such as Mathematics, Statistics, Computer Science, Economics or Actuarial Science.
• Four to six years of experience in data analytics, forensic analytics, fraud monitoring or financial‑crime investigation, preferably in financial services.
• Certification in data analytics or forensic technology tools; professional certifications such as CFE, CPA(K), CFA or CISSP are an advantage.

Required skills

• Advanced SQL and working proficiency in Python or R.
• Experience with forensic analytics, anomaly detection, statistical testing and link analysis.
• Proficiency in visualisation tools such as Power BI, Tableau or Qlik Sense.
• Familiarity with forensic tools like CaseWare IDEA, Alteryx or Cellebrite.
• Knowledge of relational databases, ETL processes and ERP data structures.